There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

物件導向軟體之架構(Object-Oriented Software Construction)探討

http://user.it.uu.se/~jarst116/slides/week4.pdf

Graph-Based Algorithms for Boolean Function Manipulation

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

The ongoing coronavirus disease 2019 (COVID-19) pandemic poses a severe threat to public health worldwide. We combine data on demography, contact patterns, disease severity, and health care capacity and quality to understand its impact and inform strategies for its control. Younger populations in lower-income countries may reduce overall risk, but limited health system capacity coupled with closer intergenerational contact largely negates this benefit. Mitigation strategies that slow but do not interrupt transmission will still lead to COVID-19 epidemics rapidly overwhelming health systems, with substantial excess deaths in lower-income countries resulting from the poorer health care available. Of countries that have undertaken suppression to date, lower-income countries have acted earlier. However, this will need to be maintained or triggered more frequently in these settings to keep below available health capacity, with associated detrimental consequences for the wider health, well-being, and economies of these countries.

The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries.

Measles and rubella elimination in the WHO Region for Europe: progress and challenges

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1469-0691.12584

The state of measles and rubella in the WHO European Region, 2013

In this paper, we present a system based on feature extraction techniques and image segmentation techniques for detecting and diagnosing abnormal patterns in breast thermograms The proposed system consists of three major steps: feature extraction, classification into normal and abnormal pattern and segmentation of abnormal pattern Computed features based on gray-level co-occurrence matrices are used to evaluate the effectiveness of textural information possessed by mass regions A total of 20 GLCM features are extracted from thermograms The ability of feature set in differentiating abnormal from normal tissue is investigated using a Support Vector Machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier To evaluate the classification performance, five-fold cross validation method and Receiver operating characteristic analysis was performed The verification results show that the proposed algorithm gives the best classification results using K-Nearest Neighbor classifier and a accuracy of 925% Image segmentation techniques can play an important role to segment and extract suspected hot regions of interests in the breast infrared images Three image segmentation techniques: minimum variance quantization, dilation of image and erosion of image are discussed The hottest regions of thermal breast images are extracted and compared to the original images According to the results, the proposed method has potential to extract almost exact shape of tumors

Thermography Based Breast Cancer Detection Using Texture Features and Minimum Variance Quantization

Background Breast cancer is the most common malignancy in women. It is often characterized by a lack of early symptoms, which results in late detection of the disease. Detection at advanced stages of the decease implies the treatment is more difficult and uncertain. The appropriate screening programs have been conducted within the organized preventive examinations and have made significant contributions to the early breast cancer detection. Objective It is necessary to improve the screening process in order to reduce the percentage of female population that is not covered by screening programs and increase the number of early-detected breast cancers. The improvement of the screening program may be reflected in the following: more efficient determination of the list of the women who have to undergo preventive examination, introduction of screening program in thermography as a diagnostic method applied in pre-screening stage, more efficient analysis of mammograms and continuous follow up of patients. Methods The identification of target population for breast cancer screening program has been based on the age of women. The improvement of the early breast cancer diagnosis process proposed in this paper is reflected in more efficient determination of the group of women who have to undergo preventive examination based on the factors affecting the occurrence of breast cancer. Inclusion of the pre-screening phase in which thermal imaging could be applied and software support to mammographic detection of tumor are suggested. Results This paper describes the breast cancer, current screening program and techniques for early-stage breast cancer detection, module of medical information system MEDIS.NET for creating screening list based on the analysis of risk factors affecting the occurrence of breast cancer, mammography and role of thermal imaging in the process of early breast cancer detection. It also presents an overview on important achievements in computer-aided detection and diagnosis of breast cancer in mammography and thermography. Conclusions Based on the obtained results, dynamics of preventive examinations for particular groups of women that is different from the standard two-year examinations, can be successfully defined. It can be concluded that the use of a computer system for tumor diagnosis in mammogram based on various methods of image processing can help doctors in decision-making, while the use of thermal imaging in the pre-screening phase would significantly reduce the list of women for screening mammograms.

Early diagnosis and detection of breast cancer.

In this paper, we present a system based on feature extraction techniques for detecting abnormal pat- terns in digital mammograms and thermograms. A com- parative study of texture-analysis methods is performed for three image groups: mammograms from the Mammo- graphic Image Analysis Society mammographic database; digital mammograms from the local database; and ther- mography images of the breast. Also, we present a proce- dure for the automatic separation of the breast region from the mammograms. Computed features based on gray-level co-occurrence matrices are used to evaluate the effective- ness of textural information possessed by mass regions. A total of 20 texture features are extracted from the region of interest. The ability of feature set in differentiating abnormal from normal tissue is investigated using a sup- port vector machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier. To evaluate the classifica- tion performance, five-fold cross-validation method and receiver operating characteristic analysis was performed.

Dragan Jankovic

Papers

Measles and rubella elimination in the WHO Region for Europe: progress and challenges

The state of measles and rubella in the WHO European Region, 2013

Thermography Based Breast Cancer Detection Using Texture Features and Minimum Variance Quantization

Early diagnosis and detection of breast cancer.

Comparative analysis of breast cancer detection in mammograms and thermograms.